We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.
|Number of pages||8|
|Journal||Pattern Recognition Letters|
|State||Published - Apr 15 2014|
Bibliographical noteFunding Information:
This study was supported by Grants, CMMI-1100735 and IIS-1218712 , from the National Science Foundation .
- Confidence interval
- Least squares support vector machine
- Mixed effect modeling
- Prediction interval
- Semiparametric function estimation
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence